Improvement of snow physical parameters retrieval using SAR data in the Arctic (Svalbard)
Bibliographic record
Abstract
Arctic snow cover dynamics offer a changing face in terms of temporal duration and water equivalent, due to recent climate change conditions (Callaghan et al., 2011; Lemke & Jacobi, 2011). In this context, innovative methods are helpful to enhance management of the snow-pack resource for climate research, hydrology and human activities. The characteristics of Arctic snow are different from “temperate” snow (i.e. the Alps), in terms of thickness, internal structure, thermal conductivity, and metamorphism. Ground observation often indicates wind slab at the snow surface, internal rounded grains, depth hoar at the bottom, and often internal ice layer or at the interface with ground surface (Dominé et al., 2016). This work is part of the “Precip-A2” project (OSUG, Grenoble-France), focusing on snow and its interaction with the atmosphere, especially in terms of chemistry, radiative processes and precipitation. The the focused area is Ny-Ålesund, Svalbard, Norway (N 78°55’ / E 11° 55’). One subtask of the project is dedicated to X-band radar measurements (ground and spaceborne) to retrieve physical properties of arctic snow. Active radar (SAR) images are used in this project, as they do not suffer of clouds coverage and polar night, unlike optical sensors. Snow mapping at the melting season is well documented, due to the liquid water content at the snow surface (Nagler et al., 2000), dry snow height retrieval is only possible at the moment under the full polarimetric mode of the Radarsat-2 satellite, Canada (Dedieu et al., 2014; 2017).The aims of our specific task is to improve an innovative and recent method to retrieve snow depth from SAR image decomposition (Leinss, 2014), and to validate the output results with a consistent ground network, including a large international partnership (Fr, De, No, It). A set of 10 SAR images was provided by the German Space Agency (DLR) during winter 2017 from the TerraSAR-X sensor (3.1 cm, 9.6 GHz) in dual co-pol HH, VV (2.5 m resolution). Descending and ascending orbits were combined under 35-38° incidence angles, to avoid topographic constraints. The data were processed with a co-polar phase difference (CPD) set between HH and VV polarization, then projected to ground range by DEM from the Norwegian Polar Institute (5m resolution). A total of 400 ground measurements were used for validation, based on automatic permanent stations or manual collection. Snow height, temperature, density, and some structural information (stratigraphy) were observed on open spaces (herb tundra) and on glaciers. Some places are well documented within 20-year recording, as the Bayelva station (Boike et al., 2018) or the Austre Lovenbreen glacier (Bernard et al., 2015). Results show that the temporal evolution of the CPD values is strongly linked with the in-situ snow evolution: positive values for dry snow, negative values for recrystallization process. The best R2 correlation performances between estimated and measured snow depth are ranging from 0.51 to 0.75, assessing the interest of this method for snow mapping and hydrological application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".